Review of the application of image segmentation algorithm in medical images
Medical image segmentation is a key technology in the field of computer-aided diagnosis,whose main task is to accurately identify specific organs,tissues,or abnormal areas from the image.However,the quality of medical images is easily affected by their complex textures and imaging equipment limitations(such as noise and unclear boundaries),so traditional medical image segmentation methods are no longer able to meet practical clinical needs.With the advancement of deep learning technology,algorithms based on this field have made significant progress.This article first reviews seven traditional medical image segmentation strategies and focuses on two current mainstream deep learning methods:fully convolutional neural networks and U-Net,finally,the article also explores the challenges faced by these deep learning technologies and their possible solutions.
Deep learningMedical image segmentationFully convolutional neural networkU-Net
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